System and method of applying an arbitrary angle to reformat medical images

ABSTRACT

In accordance with the teachings described herein, systems and methods are provided for generating a seed plan for use in radiation therapy. The system includes an image database, the image database comprising image slices and a seed template database comprising seed templates. A contour engine is configured to generate target contour data to identify one or more objects within each image slice. A reslicer engine is configured to rotate the contoured image about an angle of rotation to produce a resliced contoured image, such that the resliced contoured image is resampled at an angle perpendicular to the angle of rotation and intersecting an isocenter. The system also includes a seed grid engine configured to generate a seed grid perpendicular to the angle of rotation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication No. 61/329,442, filed on Apr. 29, 2010, the entirety ofwhich is incorporated herein by reference.

FIELD

The technology described in this patent document relates generally tothe field of reformatting contoured medical images.

BACKGROUND

Contouring is the process of identifying an object within an image byoutlining or otherwise distinguishing the object from the rest of theimage. Medical images, such as CT (computed tomography), MR (magneticresonance), US (ultrasound), or PET (positron emission tomography)scans, are regularly contoured to identify certain pieces of anatomywithin the image. For example, a radiologist or oncologist may contour amedical image to identify a tumor within the image. Software tools areavailable to assist in this type of “manual” contouring, in which thephysician uses the software to create the contour by tracing theboundary of the object or objects within the image.

Three-dimensional scans, such as CT and PET scans, produce a series oftwo-dimensional (2D) image slices that together make up the 3D image.Contouring these types of 3D images typically requires individuallycontouring each of the 2D images slices, which can be a laboriousprocess. Systems and methods for contouring 3D images are disclosed inU.S. patent application Ser. No. 12/772,377, filed on May 3, 2010 andtitled “Systems and Methods for Contouring a Set of Medical Images,” andU.S. patent application Ser. No. 12/772,383, filed on May 3, 2010 andtitled “Systems and Methods for Generating a Contour for Medical Image,”which are incorporated herein by reference.

Contoured image slices are used in radiation therapy procedures to aid amedical practitioner in the planning of radiation delivery, such as theplacement of radiation containing seeds. The, contoured areas provide atarget for the placement of seeds, however, the medical practitioner maywish to conduct such a procedure at an angle different than the originalcontoured image slices.

Re-slicing is generally directed to applying an arbitrary angle torotate and reformat medical images. The angle may include a user-definedangle for reformatting images to be orthogonal to a plannedbrachytherapy seed planning path or in plane with an external beam entrypath. Re-slicing a contoured image involves manipulating both theoriginal image slices and the contoured image slices. There exists aneed to reformat image slices in an efficient manner that allowscontoured image data to translate to the reformatted image slices andtreatment planning to be performed on the reformatted image slices.

SUMMARY

In accordance with the teachings described herein, systems and methodsare provided for generating images and treatment plans for use inradiation therapy. In one example, the system may include an imagedatabase, the image database comprising image slices and a seed templatedatabase comprising seed templates. A contour engine may be configuredto generate target contour data to identify one or more objects withineach image slice. A reslicer engine may be configured to rotate thecontoured image about an angle of rotation to produce a reslicedcontoured image, such that the resliced contoured image is resampled atan angle perpendicular to the angle of rotation and intersecting anisocenter. The system may also include a seed grid engine configured togenerate a seed grid perpendicular to the angle of rotation.

In one example, a processor-implemented method for reformatting medicalimage slices may include the steps of receiving one or more imageslices, the image slices comprising one or more cross sectional medicalimages; contouring each image slice to generate target contour data toidentify one or more objects within each image slice; defining anisocenter and a needle angle of the image slices, the isocenter defininga center point of a target mass and the needle angle defining the angleof entry for a set of needles; and rotating the contoured image slicesabout the needle angle to produce a resliced contoured image, theresliced contoured image being the image slice rotated at an angleperpendicular to the needle angle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 6 depict block diagrams of example systems for reslicing aset of medical images.

FIGS. 2-5 illustrate examples of contours and resliced images that maybe generated by the systems and methods described herein.

FIG. 7 depicts an example plan verification image displaying seedmigration.

FIG. 8 depicts an example dose volume histogram chart comparing dosagelevels in a target treatment area.

FIGS. 9-12 depict flow diagrams of example methods for reslicing a setof medical images.

FIG. 13 depicts an example system for reslicing medical images.

FIG. 14 is a block diagram of hardware which may be used to implementthe various embodiments described herein.

DETAILED DESCRIPTION

FIG. 1 depicts a block diagram of an example system 100 for reslicing aset of medical images to generate a treatment plan. The system 100 usesmedical image data and applies contours to locate a target mass area anda treatment area. The contoured image data is then resliced, or rotated,along a defined needle path or angle of rotation. The rotated imageprovides a medical practitioner with a needle's eye view of a radiationtreatment procedure. In this view, a seed grid is overlaid on theresliced image, allowing the medical practitioner to develop a treatmentplan with seed therapy or external beam radiation therapy. As part ofthe process, the system 100 may produce a treatment plan that detailsthe contoured areas, seed or beam locations and strengths, dosage levelsfor treatment, and effectiveness of the proposed plan.

The system 100 includes a contouring block 102, an image and contourreslicing block 104, a seed grid generator 106, a seed plan generator108, and a treatment data plan 110. Also included in the example system100 are a medical image database 112 for storing a set oftwo-dimensional image slices, a seed grid template database 114 forstoring seed grid template data, and inputs 116-124. It should beunderstood that contouring block 102, image and contour reslicing block104, seed grid generator 106, and seed plan generator 108, as describedherein, may be implemented by software instructions executing on one ormore processing devices. In other implementations, however, one or moreoperations of these software engines may instead be performed by otherknown mechanisms such as firmware or even appropriately designedhardware. The medical image database and seed grid template database, asdescribed herein, may be implemented using one or more memory devices.For instance, in one example the medical image database and seed gridtemplate database may be implemented within the same memory device, andin another example they may be implanted on separate memory devices.

The plurality of medical images are loaded into the medical imagesdatabase 112 for contouring. The plurality of medical images may includea set of two-dimensional (2D) slices that are received, for example,from a CT scanner or other system for capturing three-dimensional (3D)medical images, such that the set of 2D slices together represent a 3Dmedical image. In other examples, the plurality of medical image slicescould be virtual, such as sagittal or coronal images (or any otherslicing angle through the image data).

In operation, the system 100 receives image slices from the medicalimages database 112 at the contouring block 102. At the contouring block102, image slices from the medical images database 112 are contouredaccording to a contour transformation engine. In one example, thecontouring block 102 illustrated in FIG. 1 could be used as asemi-automatic contouring tool that receives initial contour data forone of the image slices and then automatically contours the remainingimages slices in the set. In this example, the contouring block 102 mayreceive contour data for the initial source image from an externalsource. For instance, the initial contour data may be provided bymanually contouring one of the image slices 108 using one of a varietyof know manual contouring software applications. Based on the contouringof the initial image slice, the contouring block 102 may automaticallycontour the remaining image slices based on the contour data from theinitial image slice. As shown in FIG. 2, the contouring data may includea clinical target volume contour 202, a planning target volume contour204, and a chest wall contour 206.

Referring again to FIG. 1, the image and contour reslicing block 104receives image and contour data from the contouring block 102. With thisdata, the image and contour reslicing block 104 may define an isocenter116 and angle of rotation 118. As shown in FIG. 2, a target mass contour202 is typically applied by the contouring block. The isocenter 208defines the center point of the treatment volume.

The isocenter may also be utilized to define the angle for rotation 302of the image. With reference to FIG. 3A, the angle of rotation 302 istypically aligned to intersect with the isocenter. An isocenter 304 isselected at the center of the target mass 304, and the angle of rotation302 intersects with the isocenter 304, so that the image and contourreslicing block may rotate the image with respect to the proper area ofthe original medical image. The rotated image provides a rotatedviewpoint of the original image slices along the angle of rotation 302.For example, the angle of rotation 302 may coincide with the intendedneedle path for a radiation therapy procedure.

In order to rotate and reslice the image slices, the image and contourreslicing block 104 formulates the image slices as a 3D image cube. Theimage and contour reslicing block 104 utilizes the angle of rotation 302to build a transformation matrix to apply to the image. Thetransformation matrix is applied to the image cube voxel co-ordinates togenerate a new set of voxel co-ordinates which correspond to the voxelpositions in the new re-sliced and re-oriented image. The original imageslices are then interpolated at the new voxel coordinates to generatethe resliced image. The resliced image is sampled into a specified voxelsize and image volume dimensions.

To utilize the contours applied in the contouring block 102, the imageand contour reslicing block 104 also reslices and reorients any contoursassociated with the original image slices to the same space as theresliced image slices. The contours are formulated as either a set of 3Dmesh objects with boundaries in voxel coordinates into the originalimage space or as a 3D byte cube with each contour represented as abitmask with a nonzero bit value for voxels included in each contour anda 0 value where no contour is present. In the first formulation as 3Dmeshes, the positions can be calculated using the same transformationmatrix and transposition scheme as was used for the image. In the secondformulation, the bitmask contour is interpolated into a floating pointin the same positions as the image cube, and the resulting valuesthresholded to determine inclusion into a new bitmask contour in the newresliced, reoriented image slice. The threshold may be determined inorder to maintain the contour volume or to minimize shifts in contourcentroid.

As shown in FIG. 3B, the rotated image slices, provide a viewpointsimilar to the actual radiation therapy procedure. From this angle, thesame contours from FIG. 3A are represented, but at a different anglethan the CT scan image slices. For example, in FIG. 3B, a clinicaltarget volume contour 350, a planning target volume contour 352, and achest wall contour 354 are each illustrated. Further illustrated in FIG.3B is the isocenter 356. The isocenter 356 also represents the entrypoint and needle path or the point at which all external treatment beamsintersect for a radiation treatment procedure.

With reference again to FIG. 1, the image and contour reslicing block104 generates resliced image and contour data, which is received by theseed grid generator 106. At the seed grid generator 106, seed gridtemplate data is received from the seed grid template database 114 alongwith an input for selecting one or more of the seed grid templates fromthe seed grid template database 114. The seed grid template database 114includes seed grid templates that may be used to create a seed grid 402on the resliced image, as shown in FIG. 4.

FIG. 4 illustrates a seed grid 402 overlaid on a resliced contouredimage slice, such as the example from FIG. 3B. The image contour slicesportrayed in FIG. 3B are also represented in FIG. 4. For example, aclinical target volume contour 404, a planning target volume contour406, and a chest wall contour 408 are each illustrated. The seed grid402 is centered at the isocenter 410. Each image slice that has beenresliced may include a seed grid 402. Including a seed grid 402 at eachpossible image slice allows for a treatment plan to be accuratelyplanned at different depth levels of a target volume.

The seed grid 402 of FIG. 4 may correspond to a physical template usedby a medical practitioner during a radiation treatment procedure. Eachinsertion point 412 on the seed grid 402 may represent a location for aseed needle during the radiation therapy procedure. Thus, providing aseed grid 402 on a medical image slice that corresponds to a physicalseed template allows a medical practitioner to have a virtual guide toeach needle insertion point. The efficiency and accuracy of a seedtreatment procedure is improved because the system 100 of FIG. 1 has thecapability to formulate a treatment plan for the specific seed templatechosen for the procedure.

Referring back to FIG. 1, after a seed grid template is selected at theseed grid generator 106, the seed plan generator 108 creates a treatmentplan comprising treatment plan data 110. A treatment plan includes atleast the contour image data on a resliced image, a seed grid template,seed grid position 122, and dosing plan 124. With reference to FIG. 5,the seed plan generator 108 of FIG. 1, configures a treatment planincluding seeds 502 on selected insertion points 504 of the seed grid500. Each seed 502 may be placed at any of the insertion points 504.When a seed 502 is placed on the seed grid 500, dose level markers 506a-c indicate the approximate area that will receive treatment. Anynumber or combination of dose level markers 506 a-c may be used toindicate different dose levels for a treatment plan. If a seed 502 isadded or removed from the seed grid 500, the seed plan generator may beconfigured to automatically adjust the dose level markers 506 a-c toindicate the new plan of treatment. Similarly, FIG. 5 illustrates onlyone depth, or one resliced image. If a radiation treatment plan uses,for example, seeds at varying lengths on a needle, a seed may appear atone insertion point 504 on a resliced image at 5 mm depth, but notappear on a resliced image at 10 mm depth. Therefore, the dose levelmarkers 506 a-c may be automatically adjusted for each resliced image.

FIG. 6 depicts a block diagram of an example system 600 for reslicing aset of medical images and optimizing a plan of treatment. The system 600includes a contouring block 602, an image and contour reslicing block604, a seed grid generator 606, a seed plan generator 608, a planoptimizer 610, an optimized plan 612, and report data 614. Also includedin the example system 600 are a medical image database 616 for storing aset of two-dimensional image slices, a seed grid template database 618for storing seed grid template data, and inputs 620-632. The contouringblock 602, image and contour reslicing block 604, seed grid generator606, seed plan generator 608, medical images database 616, and seed gridtemplate database 618 operate as described above with reference to FIGS.1-5. In addition to these components, the system 600 of FIG. 6 includesplan optimizer 610. The plan optimizer 610 receives the treatment plandata from the seed plan generator 608. The plan optimizer also receivesa set of constraints 630 and a set of parameters 632 and may utilizethese inputs to determine if the treatment plan data is optimized.

To determine whether the treatment plan is optimized, the plan optimizer610, compares the treatment plan data to the one or more constraints 630and parameters 632 to determine if the treatment plan data is within arequired parameter error margin of the constraints 630. The planoptimizer 610 may utilize any method of optimization known to thoseskilled in the art to perform the comparison. Any number of constraints630 may be used and FIG. 6, for example, includes needle count orposition, seed count or relative position, seed migration ordisplacement models, and dosage to contours as constraints. The planoptimizer may be configured to compare the treatment plan data, whichincludes the seed locations, dosage level indicators, and seed grid foreach image slice, to the selected constraints 630.

If the plan optimizer 610 chooses to compare seed migration data, it mayutilize data from a medical image such as the one shown in FIG. 7. FIG.7 represents medical image data at a point in time during or after aradiation treatment procedure has been performed. The exampleillustrated in FIG. 7 shows the location of seeds 702 following aradiation seed therapy procedure. The location of the seeds 702 may becompared with the location of a seed in FIG. 5. Because seeds maydisplace during or migrate following insertion, this comparison isuseful to predict where a seed may migrate so that it stays within thetarget treatment area. If the current seed locations indicated in atreatment plan would allow the target volume to be underdosed orsurrounding tissues to be overdosed because of likely seed displacementsand migrations, the plan optimizer 610 of FIG. 6 may automaticallycorrect the seed locations to a better seed insertion point. Likewise,the plan optimizer 610, may return the system to the seed grid generator606 to perform the seed and dose procedure again.

As described above, the medical image data illustrated in FIG. 7 may,for example, be a post-implant verification image. In order to obtainthe image data shown in FIG. 7, it may be advantageous to reslice thepost-implant image prior to seed identification on the verificationimage if the seeds were placed at an angle to the imaging slices. Thisis analogous to the example described above with reference to FIG. 1,except there is no seed grid or template. In this way, the seeds may beidentified/placed freely in the image.

Along with the optimization process, the plan optimizer of FIG. 6 mayproduce report data 614. The report data 614 may include any combinationof statistics, operating parameters, image slices, and graphs. Forexample, shown in FIG. 8 is an example dose volume histogram outputgraph comparing the conformance of the dose levels in the treatment planto the target mass. The graph compares the percent coverage 802 of acontour area to the dose level 804 in the selected area. FIG. 8 will bediscussed in conjunction with FIG. 4 as a reference. For instance, line806 a represents the dose level in a target mass contour 404, line 806 brepresents the dose level in a treatment area contour 406, line 806 crepresents the dose level in a chest wall contour 408, and line 806 drepresents the dose level occurring outside of all contour areas. Asshown in the graph of FIG. 8, the highest dosage level should appear inthe target mass contour 404 and the lowest dosage level should appearoutside of all contour areas. The results of the graph in FIG. 8 may beincluded in the report data 614 of FIG. 6 so that a seed treatment planmay be manually optimized to ensure the most effective treatment.

With reference again to FIG. 6, if the treatment plan data aligns withthe selected constraints within the parameter margin, then the planoptimizer 610 may output report data 614 and an optimized plan 612. Ifthe treatment plan data falls outside of the selected parameter marginsto the constraints 630, then the system 600 is configured to adjust theseed plan. The system 600 may be configured to either automaticallyadjust the seed plan based on the plan optimizer 610, or may be manuallyadjusted based on the report data 614.

Regardless of either method of adjustment, the seed plan is adjusted byreturning to the seed grid generator 606, where a new seed grid may beselected 624 from the seed grid template database 618. As describedabove with reference to FIG. 1, the seed plan generator 608 generates atreatment plan according to the seed grid position 626 and the dosage628. This treatment is received by the plan optimizer 610 to perform theoptimization techniques described above. When a treatment plan isoptimal, the plan optimizer 610 outputs report data 614 and an optimizedplan 612.

FIG. 9 is a flow diagram depicting an example method of reslicing a setof medical images. At step 902, source image slices are received. Inthis example, the image slices are 2D representations of a medical imageand have no contouring. At step 904, the method determines whether theimage slices will be contoured prior to reslicing. If the image slicesare to be contoured prior to reslicing, the method moves to step 906 andcontours the image slices. After contouring the image slices anisocenter is defined at step 908 and the planning plane, or the angle ofrotation and needle direction, is defined at step 910. At step 912 thecontoured image slices are rotated and resliced according to theplanning plane defined at step 910. With these resliced images, themethod is configured to generate a seed grid on the resliced andcontoured images.

If, at step 904, the image slices are not selected for contouring, themethod moves to step 916 to define an isocenter. Then, at step 918, aplanning plane is defined and utilized at step 920 to rotate and reslicethe image slices. At step 922, the method determines whether the imageslices have been contoured. If the resliced image slices have beencontoured, a seed grid may be generated on the resliced contoured imagesat step 924. If the resliced images have not been contoured, however,the method goes to step 926 for contouring. Even though the originalimage slices have already been resliced, the method again reformats theimage slices so that the contouring is applied to each resliced imageslice at step 928. In another example, step 928 may be omitted, andinstead the user may contour the resliced images with no need forfurther reslicing. In either case, the contoured image slices are thenused at step 924 where a seed grid may be generated on each reslicedcontoured image.

It should be understood that similar to the other processing flowsdescribed herein, one or more of the steps and the order in theflowchart may be altered, deleted, modified and/or augmented and stillachieve the desired outcome.

FIG. 10 is a flow diagram depicting an example method of reslicing a setof medical images and determining whether a seed plan is optimal. Atstep 1002, source image slices are received. In this example, the imageslices are 2D representations of a medical image and have no contouring.At step 1004, the method determines whether the image slices will becontoured prior to reslicing. If the image slices are to be contouredprior to reslicing, the method moves to step 1006 and contours the imageslices. After contouring the image slices an isocenter is defined atstep 1008 and the planning plane, or the angle of rotation and needledirection, is defined at step 1010. At step 1012 the contoured imageslices are rotated and resliced according to the planning plane definedat step 1010. With these resliced images, the method is configured togenerate a seed grid on the resliced and contoured images.

If, at step 1004, the image slices are not selected for contouring, themethod moves to step 1016 to define an isocenter. Then, at step 1018, aplanning plane is defined and utilized at step 1020 to rotate andreslice the image slices. At step 1022, the method determines whetherthe image slices have been contoured. If the resliced image slices havebeen contoured, a seed grid may be generated on the resliced contouredimages at step 1024. If the resliced images have not been contoured,however, the method goes to step 1026 for contouring. Even though theoriginal image slices have already been resliced, the method againreformats the image slices so that the contouring is applied to eachresliced image slice at step 1028. In another example, step 1028 may beomitted, and instead the user may contour the resliced images with noneed for further reslicing. In either case, the contoured image slicesare then used at step 1024 where a seed grid may be generated on eachresliced contoured image.

After steps 1014 or 1024 the method moves to step 1030 to populate theseed grid and generate a seed plan. At step 1030, the seed grid may bepopulated with seeds at different insertion points as described abovewith reference to FIGS. 1 and 5. Following the creation of the seedplan, step 1032 determines whether the seed plan is dosimetricallyideal. This process may be accomplished through any number of comparisonmethods. One example is the utilization of the graph of FIG. 8 todetermine the percentage of a dose level at different contour areas. Ifthe method determines at step 1032 that an insufficient percentage ofthe target area is receiving the prescription dose or the dose isimbalanced around the target, then it moves to step 1036 where theisocenter and planning plane may be redefined. Redefining the isocenterand planning plane may provide for a better reslicing of the imageslices so that a more accurate seed plan may be generated. After step1036, the method returns to step 1020 and the process is repeated asnecessary until the dose plan is dosimetrically ideal and the method mayfinish at step 1034.

FIG. 11 is a flow diagram depicting an example method of reslicing a setof medical images to automatically generate a seed plan based on seedmigration data. At step 1102, source image slices are received. In thisexample, the image slices are 2D representations of a medical image andhave no contouring. At step 1104, the method determines whether theimage slices will be contoured prior to reslicing. If the image slicesare to be contoured prior to reslicing, the method moves to step 1106and contours the image slices. After contouring the image slices anisocenter is defined at step 1108 and the planning plane, or the angleof rotation and needle direction, is defined at step 1110. At step 1112the contoured image slices are rotated and resliced according theplanning plane defined at step 1110. With these resliced images, themethod is configured to generate a seed grid on the resliced andcontoured images.

If, at step 1104, the image slices are not selected for contouring, themethod moves to step 1116 to define an isocenter. Then, at step 1118, aplanning plane is defined and utilized at step 1120 to rotate andreslice the image slices. At step 1122, the method determines whetherthe image slices have been contoured. If the resliced image slices havebeen contoured, a seed grid may be generated on the resliced contouredimages at step 1124. If the resliced images have not been contoured,however, the method goes to step 1126 for contouring. Even though theoriginal image slices have already been resliced, the method againreformats the image slices so that the contouring is applied to eachresliced image slice at step 1128. These resliced contoured image slicesare then used at step 1124 where a seed grid may be generated on eachresliced contoured image.

After steps 1114 or 1124 the method moves to step 1130 to automaticallygenerate a seed plan based on seed migration data. At step 1130, themethod receives seed plan deflection and migration data from one or moredatabases. The seed plan migration data includes information related tothe planned implant location or initial implant location of a seed anddata related to the location of a seed at a point in time after theinitial implant procedure. This data allows for a determination of anoptimal implant location based on how seeds may migrate from the initialor planned implant location. The method then generates an optimal seedplan accounting for any possible migration of each seed.

The seed migration data may require a method of identifyingcorresponding seeds in the treatment plan with seeds in a post-implantverification image, for example as shown in FIG. 7. The method forcorrelating seeds may include a seed registration algorithm which seeksto minimize differences in the relative position of the seeds whencomparing the seed plan to the post-implant verification image. The seedmigration data could be used, for example, to generate a seed migrationmodel which defines the likelihood that each seed will deflect ormigrate from the planned implant location. This model can then be usedto describe the likely dosimetric outcome of a medical practitionerattempting to place a seed at a given location.

FIG. 12 is another example flow diagram depicting an example method ofreslicing a set of medical images to generate a seed plan based on seedmigration data. The method of FIG. 12 begins at step 1202 where a seedgrid has been generated on an image, possibly a resliced and reformattedimage. At step 1204, the seed grid is populated with the dose plan and aseed plan is generated. This generated seed plan is then compared, atstep 1206, with seed plan migration data. A comparison between thegenerated seed plan and the seed plan migration data occurs at step1208. A predefined set of constraints and parameters allows the methodto determine whether the generated seed plan is optimized based on howthe seeds may migrate after implantation. If the method determines thatthe generated seed plan is optimized, then the seed plan is outputted atstep 1210. This seed plan may include data which indicates the range oflikely delivery outcomes of the plan given the seed migration model. Forexample, a dose volume histogram graph may include error bars indicatingthe range of likely dose levels for each possible output. If, however,the generated seed plan is not optimized based on the seed planmigration data, the seed plan is adjusted at step 1212. The process atsteps 1206 and 1208 is then repeated until the seed plan is optimizedbased on the seed plan migration data.

FIG. 13 depicts a block diagram of an example system 1300 for reslicinga set of medical images to generate a seed plan. The system includes aseed template generation system 1302, a contour data and image slicesdatabase 1304, a template data database 1306, and a display 1308. Theseed template generation system further includes a contour engine 1310,a reslicer engine 1312, resliced and contoured image 1314, and atreatment plan engine 1316.

In operation, a set of medical image slices from the contour data andimage slices database 1304 are loaded onto the reslicer engine 1312 orthe contour engine 1310. The contour engine 1310 is configured tocontour a set of medical image slices. The contours may include, forexample, a clinical target volume contour 202, a planning target volumecontour 204, and a chest wall contour 206, as illustrated in FIG. 2. Thereslicer engine 1312 is configured to define an isocenter and angle ofrotation to reslice the image slices. This process may occur eitherbefore or after the image slices are contoured by the contour engine1310. Once an isocenter and angle of rotation are defined, the reslicerengine is further configured to rotate and reformat the image slicesalong the angle of rotation. The output of the process is the reslicedand contoured image 1314.

The treatment plan engine 1316 receives the resliced and contoured image1314 and may perform a number of operations related to the placement andselection of a seed grid. The treatment plan engine 1316 receives a seedgrid template from the template data database 1306. Once a seed gridtemplate is selected, the seed grid engine formats the resliced imageslices with the selected grid. The treatment plan engine 1316 may alsobe further configured to utilize the selected grid to produce a seedtreatment plan, as described in detail above with reference to FIGS. 1and 5.

Instead of producing a seed grid template on a resliced image, thetreatment plan engine 1316 may be configured to produce a treatment planfor use in an external beam radiation therapy. For such a treatment,treatment beams are used by the medical practitioner for the radiationtherapy. In a procedure using treatment beams, the resliced images maystill be utilized, however, a seed grid and physical templatecorresponding to the seed grid template is not utilized. The treatmentplan engine 1316 may also incorporate a plan optimizer which makes useof the model correlating the original plan with the verification images.

FIG. 14 is a block diagram of hardware 1410 which may be used toimplement the various embodiments described herein. The hardware 1410may be a personal computer system or server system that includes acomputer having as input devices keyboard 1414, mouse 1416, andmicrophone 1418. Output devices such as a monitor 1420 and speakers 1422may also be provided. The reader will recognize that other types ofinput and output devices may be provided and that the present inventionis not limited by the particular hardware configuration.

Residing within computer 1420 is a main processor 1424 which iscomprised of a host central processing unit 1426 (CPU). Softwareapplications 1427 may be loaded from, for example, disk 1428 (or otherdevice), into main memory 1429 from which the software application 1427may be run on the host CPU 1426. The main processor 1424 operates inconjunction with a memory subsystem 1430. The memory subsystem 1430 iscomprised of the main memory 1429, which may be comprised of a number ofmemory components, and a memory and bus controller 1432 which operatesto control access to the main memory 1429. The main memory 1429 andcontroller 1432 may be in communication with a graphics system 1434through a bus 1436. Other buses may exist, such as a PCI bus 1437, whichinterfaces to I/O devices or storage devices, such as disk 1428 or aCDROM, or to provide network access.

This written description uses examples to disclose the invention,including the best mode, and also to enable a person skilled in the artto make and use the invention. The patentable scope of the invention mayinclude other examples that occur to those skilled in the art.

For instance, the systems and methods described herein, such as themethod of relating treatment plans to delivered treatments based inverification images, can be applied to brachytherapy seed plans as wellas other areas, such as external beam radiation therapy (EBRT). In thecase of EBRT, the verification image may come from an imaging systemdirectly related to the treatment room. For example, a cone-beam CT maybe attached to the linear accelerator in the treatment room. Theverification image can then be related to the original treatment planusing any model or image registration technique, such as deformableimage registration, in order to develop a model for treatment deliveryuncertainty analogous to a seed migration model. More specifically,deformable registration could be used to correlate each voxel in theplanning image with the corresponding anatomy in a set of verificationimages. With dose calculation performed on the verification images, eachvoxel in the planning image would have a planned dose and a delivereddose. A patient-specific or population-based model could be trained witha set of the correlated planned and verification doses which woulddescribe the likely variation in actual dose delivered for each voxel ina patient's anatomy. To develop a population-based model, thevoxel-level positional or dose variations could be related to oneanother by deformable registration to a common atlas, which could thenbe registered to a patient planning image. This model could then be usedfor plan optimization for future treatment fractions for the samepatient or for optimization of treatment plans for other patients.

It is further noted that the systems and methods described herein may beimplemented on various types of computer architectures, such as forexample on a single general purpose computer or workstation, or on anetworked system, or in a client-server configuration, or in anapplication service provider configuration.

Additionally, the methods and systems described herein may beimplemented on many different types of processing devices by programcode comprising program instructions that are executable by the deviceprocessing subsystem. The software program instructions may includesource code, object code, machine code, or any other stored data that isoperable to cause a processing system to perform methods describedherein. Other implementations may also be used, however, such asfirmware or even appropriately designed hardware configured to carry outthe methods and systems described herein.

The systems' and methods' data may be stored and implemented in one ormore different types of computer-implemented ways, such as differenttypes of storage devices and programming constructs (e.g., data stores,RAM, ROM, Flash memory, flat files, databases, programming datastructures, programming variables, IF-THEN (or similar type) statementconstructs, etc.). It is noted that data structures describe formats foruse in organizing and storing data in databases, programs, memory, orother computer-readable media for use by a computer program.

The systems and methods may be provided on many different types ofcomputer-readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) thatcontain instructions for use in execution by a processor to perform themethods' operations and implement the systems described herein.

The computer components, software modules, functions, data stores anddata structures described herein may be connected directly or indirectlyto each other in order to allow the flow of data needed for theiroperations. It is also noted that a module or processor includes but isnot limited to a unit of code that performs a software operation, andcan be implemented for example as a subroutine unit of code, or as asoftware function unit of code, or as an object (as in anobject-oriented paradigm), or as an applet, or in a computer scriptlanguage, or as another type of computer code. The software componentsand/or functionality may be located on a single computer or distributedacross multiple computers depending upon the situation at hand.

It is claimed:
 1. A processor-implemented method for reformattingmedical images to facilitate generation of a plan for a medicalprocedure, comprising: receiving a first set of medical images of athree-dimensional (3D) volume, the first set of medical imagescomprising two-dimensional (2D), cross-sectional slices of the 3Dvolume; contouring one or more medical images, of the first set ofmedical images, to generate target contour data which identifies anobject within the 3D volume; and generating a second set of medicalimages, usable for planning the medical procedure, by reslicing the 3Dvolume according to a planning plane, wherein the planning plane isestablished by an isocenter of the object within the 3D volume and anormal vector extending from a viewpoint of the medical procedure to theisocenter.
 2. The method of claim 1, further comprising: generating agrid template on a medical image of the second set of medical images,the grid template including indicators for possible interaction pointsfor the medical procedure; and generating a plan for the medicalprocedure based on the grid template, the plan for the medical procedureidentifies a set of indicators of the grid template selected as actualinteraction points.
 3. The method of claim 2, wherein the plan for themedical procedure is a treatment plan for brachytherapy, the possibleinteraction points are possible seed insertion points, and the set ofindicators include selected seed insertion points among the possibleseed insertion points.
 4. The method of claim 3, wherein the treatmentplan further identifies one or more dose level markers, based on the setof indicators selected, that indicate an approximate area to receivetreatment.
 5. The method of claim 4, further comprising displaying theset of indicators and the one or more dose level markers on the medicalimage, of the second set of medical images, on which the grid templateis generated.
 6. The method of claim 3, further comprising: comparingthe treatment plan to a set of constraints; and determining whether thetreatment plan is within an error margin specified by the set ofconstraints.
 7. The method of claim 6, further comprising adjusting thetreatment plan when the treatment plan is determined to be outside theerror margin specified by the set of constraints.
 8. The method of claim6, wherein the set of constraints include one or more of a seed countconstraint, a constraint on relative positioning of seeds, a seeddisplacement model, or a constraint on a dose level relative to acontour.
 9. The method of claim 3, further comprising: generating a seedmigration model based on medical image data acquired after a radiationtherapy procedure is performed, the seed migration model indicatingrespective displacements of seeds from respective insertion locations;and adjusting the treatment plan, based on the seed migration model, toaccount for seed migration to maintain dosing in accordance with adosing plan.
 10. The method of claim 1, wherein the viewpoint lies alonga path of at least one of a needle or a radiation beam.
 11. The methodof claim 1, wherein the medical procedure is a biopsy.
 12. The method ofclaim 1, wherein the second set of medical images comprises 2D,cross-sectional slices of the 3D volume along a set of planes which areco-planar with or parallel to the planning plane.
 13. The method ofclaim 12, wherein reslicing the 3D volume to generate the second set ofmedical images comprises interpolating image data, from the first set ofmedical images, at respective voxel positions of the 3D volume whichrespectively intersect respective planes of the set of planes.
 14. Themethod of claim 12, further comprising applying the target contour datato identify the object in the second set of images, wherein applying thetarget contour data comprises interpolating target contour data atrespective voxel positions of the 3D volume which respectively intersectrespective planes of the set of planes.
 15. A system for reformattingmedical images to facilitate generation of a plan for a medicalprocedure, the system comprising: a non-transitory computer-readablestorage medium having stored thereon an image database for storing afirst set of medical images of a three-dimensional (3D) volume, thefirst set of medical images comprising two-dimensional (2D),cross-sectional slices of 3D volume; and at least one processorconfigured to execute software instructions stored on the non-transitorycomputer-readable storage medium, the processor thereby being configuredas: a contour engine configured to generate target contour data thatidentifies an object within the 3D volume; and a reslicer engineconfigured to slice the 3D volume according to a planning plane togenerate a second set of medical images which are employed to plan themedical procedure, wherein the planning plane is established by anisocenter of the object within the 3D volume and a normal vectorextending from a viewpoint of the medical procedure to the isocenter.16. The system of claim 15, wherein the non-transitory computer-readablestorage medium further stores thereon a grid template database forstoring one or more grid templates respectively including indicators forpossible locations of interaction points for the medical procedures; andthe at least one processor is further configured as: a grid engineconfigured to apply a grid on a medical image of the second set ofmedical images, the grid corresponds to a grid template from the gridtemplate database; and a plan engine configured to generate a plan forthe medical procedure based on the grid, the plan for the medicalprocedure identifies a set of indicators, of the grid, selected asactual interaction points.
 17. The system of claim 16, wherein the planfor the medical procedure is a biopsy plan.
 18. The system of claim 16,wherein the plan for the medical procedure is a treatment plan forbrachytherapy, the possible locations of interaction points are possibleseed insertion points, and the set of indicators include selected seedinsertion points among the possible seed insertion points.
 19. Thesystem of claim 18, wherein the treatment plan further identifies one ormore dose level markers, based on the set of indicators selected, thatindicate an approximate area to receive treatment.
 20. The system ofclaim 19, wherein the plan engine is further configured to overlay theset of indicators and the one or more dose level markers on the medicalimage, of the second set of medical images, on which the grid isgenerated.
 21. The system of claim 18, wherein the grid engine isfurther configured to compare the treatment plan to a set of constraintsand determine whether the treatment plan is within an error marginspecified by the set of constraints.
 22. The system of claim 21, whereinthe processor is further configured to operate as a plan optimizerengine configured to adjust the treatment plan when the treatment planis determined to be outside the error margin specified by the set ofconstraints.
 23. The system of claim 21, wherein the set of constraintsinclude one or more of a seed count constraint, a constraint on relativepositioning of seeds, a seed displacement model, or a constraint on adose level relative to a contour.
 24. The system of claim 22, whereinthe plan optimizer engine is further configured to: generate a seedmigration model based on medical image data acquired after a radiationtherapy procedure is performed, the seed migration model indicatingrespective displacements of seeds from respective insertion locations;and adjust the treatment plan, based on the seed migration model, toaccount for seed migration to maintain dosing in accordance with adosing plan.
 25. The system of claim 15, wherein the viewpoint liesalong a path of at least one of a needle or a radiation beam.
 26. Thesystem of claim 15, further comprising a display to output the medicalimage of the second set of medical images.
 27. A processor-implementedmethod for reformatting medical image data in accordance with aprocedure, the method comprising: receiving a first set of medicalimages of a three-dimensional (3D) volume, the first set of medicalimages comprising two-dimensional (2D), cross-sectional slices of the 3Dvolume; contouring one or more medical images, of the first set ofmedical images, to generate target contour data which identifies anobject within the 3D volume; identifying an isocenter of the object andan angle, wherein the isocenter and the angle define a planning planewithin the 3D volume; and generating a second set of medical images,usable for planning the medical procedure, by reslicing the 3D volumeaccording to the planning plane, wherein the planning plane isorthogonal to a vector extending from a viewpoint of the procedure tothe isocenter.
 28. The method of claim 27, further comprising applyingthe target contour data to the second set of medical images.
 29. Themethod of claim 27, wherein the angle represents an angle between thevector and a path of at least one of a needle or radiation beam.